Abstract: One or more techniques for optimizing cancer classification based on covariate characteristics is disclosed. In a first approach, an analytics system may determine separate cutoff thresholds for positively detecting disease signal for different labels for a covariate characteristic. The system may subdivide training samples based on their labels for the covariate characteristic, to separately determine the cutoff thresholds. In other approaches, the system may train disparate classifiers for each population. The system separates the training samples based on their labels for the covariate characteristic, and separately trains classifiers to generate a signal vector representing an amount of disease signal detected in a sample. The classifiers may be trained on different feature sets as determined based on mutual information gain, genomic region coverage, and healthy activation fraction.

Abstract: A device receives first transaction information associated with a first transaction, and a first transaction account utilized for the first transaction and associated with a first financial institution. The device determines, based on a fraud model, that the first transaction is to be denied due to potential fraud associated with the first transaction account and receives second transaction information associated with a second transaction, and a second transaction account utilized for the second transaction and associated with a second financial institution. The device processes the first transaction information and the second transaction information, with a matching model, to determine whether the first transaction information matches the second transaction information and determines that the first transaction was incorrectly denied when the first transaction information matches the second transaction information within a predetermined threshold.

Abstract: A method for discriminating a cancer state is provided. A first dataset is obtained for a plurality of subjects having a first cancer state. Each subject has a plurality of nucleic acid methylation fragments with methylation patterns comprising CpG site methylation states. An autoencoder including an encoder and decoder is trained by evaluating the error in the autoencoder reconstruction of the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the first dataset. A second dataset is obtained for a plurality of subjects having a second cancer state. A plurality of features is identified by inputting the methylation pattern and nucleic acid sequence of each nucleic acid methylation fragment in the second dataset into the trained autoencoder and computing a score determined by the autoencoder reconstruction of the methylation pattern. The plurality of features is used to train a supervised model that discriminates a cancer state.

Abstract: Methods and systems for detecting cancer and/or determining a cancer tissue of origin are disclosed. Fragments are grouped into genomic regions, wherein a region model is trained for each genomic region. Fragments are input into the region models, and the outputs are used to generate a feature vector for cancer classification. In one embodiment, the region models are shallow neural networks configured to generate a score indicating a likelihood that a fragment is derived from a cancer biological sample. The feature vector is determined based on counts of fragments having scores above threshold scores for the various genomic regions. In another embodiment, the regions models are configured to generate a region embedding for an input methylation embedding of a fragment. The region embeddings are pooled by region and then pooled again to generate the feature vector.

Abstract: Methods and systems for detecting cancer and/or determining a cancer tissue of origin are disclosed. A multiclass cancer classifier is disclosed that is trained with a plurality of biological samples containing cfDNA fragments and at least one synthetic training sample generated from the biological samples. The analytics system generates the synthetic training sample by sampling fragments from a training sample labeled as cancer and sampling fragments from another training sample labeled as non-cancer. The sampling probability is determined based on a limit of detection of the cancer classifier, e.g., in order to generate synthetic training samples with cancer tumor fraction proximate to the limit of detection.

Abstract: In various embodiments, an analytics system uses models to determine features and classification of disease states. A disease state can indicate presence or absence of cancer, a cancer type, or a cancer tissue of origin. The models can include a binary classifier and a tissue of origin classifier. The analytics system can process sequence reads from test biological samples to generate data for training the classifiers. The analytics system can also use combinations of machine learning techniques to train the models, which can include a multilayer perceptron. In some embodiments, the analytics system uses methylation information to train the models to determine predictions regarding disease state.

Abstract: Systems and methods for cancer subject tumor fraction estimation comprise obtaining a first plurality of nucleic acid fragment sequences from the subject's liquid biological sample. The first plurality of sequences represent cell-free nucleic acids in the liquid sample. A second plurality of nucleic acid fragment sequences is obtained from the subject's tumor sample. The second plurality of sequences represent nucleic acid molecules in the tumor. Smoothed noise rates, each determined using nucleic acid fragment sequences from non-cancer samples mapping to a corresponding allele position in a plurality of allele positions, are obtained. Variant allele counts and coverages are determined for the allele positions using the first plurality of sequences. Solid variant allele fractions are determined for the plurality of allele positions using the second plurality of sequences.

Abstract: A method for providing a challenge user interface for an enterprise social network including users associated with an entity. The method can include receiving a request for a challenge leader board of users of the enterprise social network in response to a challenge being selected from a list of displayed challenges with the selected challenge having an associated point category. The challenge leader board can be based on users awarding challenge points to other users for challenges in the same point category as the point category of the selected challenge. In response to the request, challenge leader board information comprising an ordered list of users based on an amount of challenge points each user has been awarded in challenges in the same point category as the point category of the selected challenge can be obtained. The challenge leader board information can be transmitted to the user system for display.

Abstract: A method for providing a challenge user interface for an enterprise social network including users associated with an entity. The method can include receiving a request to select a specific challenge from a list of challenges a user is a participant and displayed on a user system associated with the user. Information associated with the selected challenge including points associated with an action can be obtained from one or more databases. The specific information can be transmitted to the user system for display. An action associated with the specific challenge can be received. A user profile associated with the user can be obtained from the one or more databases. In response to receiving the action, a total points parameter associated with the profile can be updated in response to receiving the action. The updated total point value can be stored in the one or more databases.

Abstract: A method for providing a challenge user interface for an enterprise social network including users associated with an entity. The method can include receiving a request for a challenge leader board of users of the enterprise social network in response to a challenge being selected from a list of displayed challenges with the selected challenge having an associated point category. The challenge leader board can be based on users awarding challenge points to other users for challenges in the same point category as the point category of the selected challenge. In response to the request, challenge leader board information comprising an ordered list of users based on an amount of challenge points each user has been awarded in challenges in the same point category as the point category of the selected challenge can be obtained. The challenge leader board information can be transmitted to the user system for display.

Abstract: A method for providing a challenge user interface for an enterprise social network including users associated with an entity. The method can include receiving a request to select a specific challenge from a list of challenges a user is a participant and displayed on a user system associated with the user. Information associated with the selected challenge including points associated with an action can be obtained from one or more databases. The specific information can be transmitted to the user system for display. An action associated with the specific challenge can be received. A user profile associated with the user can be obtained from the one or more databases. In response to receiving the action, a total points parameter associated with the profile can be updated in response to receiving the action. The updated total point value can be stored in the one or more databases.